5 citations · 7 across the 4 of their papers we have counts for
5 papers
Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk
Nick Judd, Alexandre Vaz, Kevin Paeth +5
We introduce findings and methods to facilitate evidence-based discussion about how large language models (LLMs) should behave in response to user signals of risk of suicidal thoug…
SandboxEval: Towards Securing Test Environment for Untrusted Code
Rafiqul Rabin, Jesse Hostetler, Sean McGregor +2
While large language models (LLMs) are powerful assistants in programming tasks, they may also produce malicious code. Testing LLM-generated code therefore poses significant risks…
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation
Rafiqul Rabin, Sean McGregor, Nick Judd
This paper explores the risk that a large language model (LLM) trained for code generation on data mined from software repositories will generate content that discloses sensitive i…
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
Sean McGregor, Allyson Ettinger, Nick Judd +10
In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…